SUMMARY: Identification of the amino-acid motifs in proteins that are targeted for post-translational modifications (PTMs) is of great importance in understanding regulatory networks. Information about targeted motifs can be derived from mass spectrometry data that identify peptides containing specific PTMs such as phosphorylation, ubiquitylation and acetylation. Comparison of input data against a standardized 'background' set allows identification of over- and under-represented amino acids surrounding the modified site. Conventionally, calculation of targeted motifs assumes a random background distribution of amino acids surrounding the modified position. However, we show that probabilities of amino acids depend on (i) the type of the modification and (ii) their positions relative to the modified site. Thus, software that identifies such over- and under-represented amino acids should make appropriate adjustments for these effects. Here we present a new program, PTM-Logo, that generates representations of these amino acid preferences ('logos') based on position-specific amino-acid probability backgrounds calculated either from user-input data or curated databases. AVAILABILITY AND IMPLEMENTATION: PTM-Logo is freely available online at http://sysbio.chula.ac.th/PTMLogo/ or https://hpcwebapps.cit.nih.gov/PTMLogo/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we propose a framework for personalized medicine with Reverse-Phase Protein Array (RPPA) and drug sensitivity. The goal of personalized medicine is to provide an optimal drug to a patient by predicting the drug sensitivity. For the prediction, our method is based on naive Bayes classifier assuming that all features (proteins) are independent. Once the classifier is trained by RPPA data for the cancer, the sensitivity of target drug is predicted for a patient's sample. As a result, a set of drugs that has low sensitivity can be provided to a patient. Through this individualized therapy, the patients can be treated more effectively without the risk of wasting time and cost. In addition, we explore if naive Bayes classifier can be improved by using the dependency between features. To this end, learning Bayesian network is performed to infer the dependency map, and then the selected edges from the estimated network model is combined with the network structure of naive Bayes classifier. As our contribution, the experiments with lung cancer data prove that RPPA data can be used to profile patient for drug sensitivity prediction, and also our proposed personalize medicine system achieved approximately 94% prediction accuracy.

As a progressive, degenerative disease, ataxia telangiectasia (A-T) is caused by a gene mutation (ATM) and is a predisposition to cancer. Understanding the impaired signaling networks caused by ATM will help minimizing the damage and finding effective therapies. The goal of this work is to investigate the dynamic change of ATM-dependent signaling pathways under the treatment of different radiation dosages. A reverse-phase protein microarray (RPPM) in conjunction with quantum dots nano-crystal technology is used for the quantitative measurement. To discover the proteomic pathways affected in ATM cells, a new hill climbing algorithm is developed based on mutual information, the classical hill-climbing method, and the optimization of the local structure. More trusted biology networks are thus defined by the new approach. The study was carried out at different time points under different dosages for cell lines with and without ATM mutation. To validate the performance of the proposed algorithm, comparison experiments were also implemented using public networks.

BACKGROUND: Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a reverse-phase protein microarray (RPPM) is used for the quantitative measurement of proteomic responses. RESULTS: To discover the signaling pathway responsive to RPPM, a new structure learning algorithm of Bayesian networks is developed based on mutual Information, conditional independence, and graph immorality. Trusted biology networks are thus predicted by the new approach. As an application example, we investigate signaling networks of ataxia telangiectasis mutation (ATM). The study was carried out at different time points under different dosages for cell lines with and without gene transfection. To validate the performance of the proposed algorithm, comparison experiments were also implemented using three well-known networks. From the experiment results, our approach produces more reliable networks with a relatively small number of wrong connection especially in mid-size networks. By using the proposed method, we predicted different networks for ATM under different doses of radiation treatment, and those networks were compared with results from eight different protein protein interaction (PPI) databases. CONCLUSIONS: By using a new protein microarray technology in combination with a new computational framework, we demonstrate an application of the methodology to the study of biological networks of ATM cell lines under low dose ionization radiation.

The goal of this study is to explore and to understand the dynamic responses of signaling pathways to low dose ionizing radiation (IR). Low dose radiation (10 cGy or lower) affects several signaling pathways including DNA repair, survival, cell cycle, cell growth, and cell death. To detect the possibly regulatory protein/kinase functions, an emerging reverse-phase protein microarray (RPPM) in conjunction with quantum dots nano-crystal technology is used as a quantitative detection system. The dynamic responses are observed under different time points and radiation doses. To quantitatively determine the responsive protein/kinases and to discover the network motifs, we present a Discriminative Network Pattern Identification System (DiNPIS). Instead of simply identifying proteins contributing to the pathways, this methodology takes into consideration of protein dependencies which are represented as Strong Jumping Emerging Patterns (SJEP). Furthermore, infrequent patterns though occurred will be considered irrelevant. The whole framework consists of three steps: protein selection, protein pattern identification, and pattern annotation. Computational results of analyzing ATM (ataxia-telangiectasia mutated) cells treated with six different IR doses up to 72 hours are presented.

Abstract Motivation: As a first step toward the elucidation of the systems biology of complex biological systems, it was our goal to mathematically model common enzyme catalytic and regulatory mechanisms that repeatedly appear in biological processes such as signal transduction and metabolic pathways. Results: We describe kMech, a Cellerator language extension that describes a suite of enzyme mechanisms. Each enzyme mechanism is parsed by kMech into a set of fundamental association–dissociation reactions that are translated by Cellerator into ordinary differential equations that are numerically solved by Mathematica™. In addition, we present methods that use commonly available kinetic measurements to estimate rate constants required to solve these differential equations. Availability: A Mathematica™ executable kMech.m file is available at the University of California, Irvine, Institute for Genomics and Bioinformatics website, http://www.igb.uci.edu/servers/sb.html. Cellerator, free of charge to academic, US government, and other non-profit organizations, can be obtained at the Cellerator website, (http://www-aig.jpl.nasa.gov/public/mls/cellerator/feedback.html). Contact: Biology correspondence should be addressed to [email protected]. Computation correspondence should be addressed to [email protected]. Supplementary information: http://www.igb.uci.edu/servers/sb.html.